Camera motion direction and speed enhance cinematic shot tools
Unveiling the Value of Motion for Cinematic Camera Trajectories
Computer Vision and Pattern RecognitionArtificial Intelligence
Summary
Camera movement is key to telling stories in movies, not just where the camera is but how it moves. The authors found that describing camera paths using direction and speed works better than just using static camera positions. They created a new way to measure how well this matches descriptions in text and built a tool called CineGEN that makes better camera movements from text. They also made a special movie clip collection with descriptions to test these ideas. This approach helps capture the artistic intent behind camera motion more clearly.
What this means in practice
- •For film production teams: Generate camera moves from script text that better capture cinematic intent using direction and speed representations.
- •For video game animators: Create realistic camera trajectories that align with narrative descriptions for immersive game scenes.
Authors
Ziqi Zhou, Yujian Yuan, Laura Sevilla-Lara
Abstract
Cinematic camera motion is a fundamental storytelling tool, defined not only by where the camera is positioned in the scene, but also by how it moves in terms of direction and speed. Recent work on camera trajectory generation and alignment to text relies on pose-centric representations. While in principle a network could derive direction of movement and speed, we find that in practice this might not happen. In fact, in this paper we discover that decomposing the camera trajectory representation from the traditional per-frame poses to direction and speed has surprising benefits across multiple tasks, including trajectory-to-text alignment as well as text-to-trajectory generation. To accurately evaluate the former, we introduce a simple and reliable protocol that overcomes the limitations of prior evaluation baselines. For the latter, building on this representational insight, we propose a novel generative model for camera trajectories, CineGEN, that achieves superior performance across a variety of metrics. We also propose a novel dataset, CineScript, containing movie clips that are enriched with scene descriptions as well as higher-level metadata. This novel data allows us to test models' ability to capture high-level cinematographic information. We show that, despite its simplicity, representing camera trajectories through direction and speed not only helps numerically to achieve better alignment and generation, but also inherently encodes complex directorial intent.